{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Group 59_19CH30018_Assignment 4",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "PhRnBMqYlib1"
      },
      "source": [
        "#Importing all the required library functions\n",
        "import pandas as pd\n",
        "import scipy.io \n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression \n",
        "import numpy as np\n",
        "from sklearn.metrics import mean_squared_error,mean_absolute_error\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import Lasso"
      ],
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CtM1OuEOJUY8"
      },
      "source": [
        "#Importing the training data(2010 Rainfall Data)\n",
        "rainfall_2010_data=scipy.io.loadmat('2010rainfall.mat')\n",
        "\n",
        "#Importing the test data(2011 Rainfall Data)\n",
        "rainfall_2011_data=scipy.io.loadmat('2011rainfall.mat')"
      ],
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gv-uSVM5LXzO",
        "outputId": "c181288b-96a6-4d97-a6b4-c38cfc0d72b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Shape of the training data\n",
        "print('Shape of the training data: ',rainfall_2010_data['XR1'].shape)\n",
        "\n",
        "#Shape of the testing data\n",
        "print('Shape of the testing data: ',rainfall_2011_data['XR'].shape)"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of the training data:  (357, 122)\n",
            "Shape of the testing data:  (357, 122)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6FTsBeA9IluF"
      },
      "source": [
        "#Function to create representation of training dataset\n",
        "def data_table_2010(data_mat):\n",
        "  days = []\n",
        "  for i in range(len(data_mat['XR1'][0])):\n",
        "    days.append(i+1)\n",
        "  location=[]\n",
        "  for i in range(len(data_mat['XR1'])):\n",
        "    location.append(i+1)\n",
        "  return pd.DataFrame(data=data_mat['XR1'],index=location,columns=days)\n",
        "\n",
        "#Function to create representation of test dataset\n",
        "def data_table_2011(data_mat):\n",
        "  days = []\n",
        "  for i in range(len(data_mat['XR'][0])):\n",
        "    days.append(i+1)\n",
        "  location=[]\n",
        "  for i in range(len(data_mat['XR'])):\n",
        "    location.append(i+1)\n",
        "  return pd.DataFrame(data=data_mat['XR'],index=location,columns=days)"
      ],
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYq5cColA9pE"
      },
      "source": [
        "#Creating a data table for Rainfall in 2010 ~ XR1\n",
        "rainfall_2010 = data_table_2010(rainfall_2010_data)\n",
        "\n",
        "#Creating a data table for Rainfall in 2011 ~ XR\n",
        "rainfall_2011 = data_table_2011(rainfall_2011_data)"
      ],
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dFl9mh3dDAX4",
        "outputId": "b7671388-1e4a-4b3a-f2ab-cb672df1eb9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "#Rainfall 2010 ~XR1\n",
        "rainfall_2010"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.170385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.611897</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.257844</td>\n",
              "      <td>56.923618</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.367834</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.235341</td>\n",
              "      <td>3.821460</td>\n",
              "      <td>5.801683</td>\n",
              "      <td>8.620442</td>\n",
              "      <td>1.493381</td>\n",
              "      <td>4.683495</td>\n",
              "      <td>7.947594</td>\n",
              "      <td>8.479486</td>\n",
              "      <td>...</td>\n",
              "      <td>1.265277</td>\n",
              "      <td>0.355482</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.537301</td>\n",
              "      <td>11.886994</td>\n",
              "      <td>8.460486</td>\n",
              "      <td>18.896152</td>\n",
              "      <td>2.159250</td>\n",
              "      <td>85.179817</td>\n",
              "      <td>8.914422</td>\n",
              "      <td>2.909730</td>\n",
              "      <td>0.414099</td>\n",
              "      <td>3.806158</td>\n",
              "      <td>2.372265</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.060152</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>38.465660</td>\n",
              "      <td>11.659060</td>\n",
              "      <td>13.772415</td>\n",
              "      <td>1.674855</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.543626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>15.400000</td>\n",
              "      <td>22.299999</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>18.700003</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>34.400005</td>\n",
              "      <td>11.800001</td>\n",
              "      <td>9.900000</td>\n",
              "      <td>10.400001</td>\n",
              "      <td>33.800003</td>\n",
              "      <td>18.400002</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>14.600001</td>\n",
              "      <td>9.9</td>\n",
              "      <td>2.7</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>10.2</td>\n",
              "      <td>12.400001</td>\n",
              "      <td>21.700001</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>13.200001</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.2</td>\n",
              "      <td>22.700003</td>\n",
              "      <td>21.400000</td>\n",
              "      <td>14.200001</td>\n",
              "      <td>0.3</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>26.600002</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>6.600000</td>\n",
              "      <td>15.200001</td>\n",
              "      <td>24.100000</td>\n",
              "      <td>25.600000</td>\n",
              "      <td>25.200001</td>\n",
              "      <td>...</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>95.800011</td>\n",
              "      <td>20.500000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>13.200001</td>\n",
              "      <td>7.600000</td>\n",
              "      <td>10.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>52.100002</td>\n",
              "      <td>46.000004</td>\n",
              "      <td>35.799999</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>96.400002</td>\n",
              "      <td>77.100006</td>\n",
              "      <td>11.400000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.5</td>\n",
              "      <td>21.100000</td>\n",
              "      <td>83.300003</td>\n",
              "      <td>6.100000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.799999</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>52.299999</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.599998</td>\n",
              "      <td>28.000002</td>\n",
              "      <td>1.700000</td>\n",
              "      <td>1.700000</td>\n",
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              "    <tr>\n",
              "      <th>354</th>\n",
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              "      <td>2.400000</td>\n",
              "      <td>12.466667</td>\n",
              "      <td>3.466666</td>\n",
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              "      <td>7.866667</td>\n",
              "      <td>6.600000</td>\n",
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              "      <td>12.266667</td>\n",
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              "      <td>9.733335</td>\n",
              "      <td>6.6</td>\n",
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              "      <td>6.8</td>\n",
              "      <td>8.266666</td>\n",
              "      <td>14.466667</td>\n",
              "      <td>2.933333</td>\n",
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              "      <td>0.8</td>\n",
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              "      <td>14.266666</td>\n",
              "      <td>9.466667</td>\n",
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              "      <td>2.133333</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>10.133334</td>\n",
              "      <td>16.066668</td>\n",
              "      <td>17.066668</td>\n",
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              "      <td>14.000000</td>\n",
              "      <td>4.133333</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>63.866669</td>\n",
              "      <td>13.666667</td>\n",
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              "      <td>8.800000</td>\n",
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              "      <td>1.733333</td>\n",
              "      <td>34.733334</td>\n",
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              "      <td>23.866667</td>\n",
              "      <td>2.133333</td>\n",
              "      <td>64.266670</td>\n",
              "      <td>51.400005</td>\n",
              "      <td>7.600000</td>\n",
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              "      <td>7.0</td>\n",
              "      <td>14.066667</td>\n",
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              "    <tr>\n",
              "      <th>355</th>\n",
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              "    <tr>\n",
              "      <th>356</th>\n",
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              "<p>357 rows × 122 columns</p>\n",
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            ],
            "text/plain": [
              "           1          2          3    ...       120       121       122\n",
              "1     0.000000   0.000000   0.000000  ...  0.000000  0.000000  0.000000\n",
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              "5     0.000000   0.000000   6.611897  ...  0.000000  0.000000  0.543626\n",
              "..         ...        ...        ...  ...       ...       ...       ...\n",
              "353  15.400000  22.299999   3.600000  ...  1.700000  1.700000  0.000000\n",
              "354  10.266666  14.866666   2.400000  ...  1.133333  1.133333  0.000000\n",
              "355   0.000000   0.000000   0.000000  ...  0.000000  0.000000  0.000000\n",
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              "357   0.000000   0.000000   0.000000  ...  0.000000  0.000000  0.000000\n",
              "\n",
              "[357 rows x 122 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJXeIv0B0FLp",
        "outputId": "b5dca549-5059-4f83-fc5d-4f1ee0df3e1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "#Rainfall 2011 ~XR\n",
        "rainfall_2011"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>13.371160</td>\n",
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              "      <td>19.586903</td>\n",
              "      <td>0.625213</td>\n",
              "      <td>22.111752</td>\n",
              "      <td>66.274300</td>\n",
              "      <td>138.969025</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>34.099937</td>\n",
              "      <td>12.399961</td>\n",
              "      <td>17.000019</td>\n",
              "      <td>68.500015</td>\n",
              "      <td>6.099988</td>\n",
              "      <td>20.499962</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.000074</td>\n",
              "      <td>0.599999</td>\n",
              "      <td>1.200005</td>\n",
              "      <td>63.799911</td>\n",
              "      <td>6.999979</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.899997</td>\n",
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              "      <td>9.399971</td>\n",
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              "      <td>58.999878</td>\n",
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              "      <td>54.399982</td>\n",
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              "      <td>43.099895</td>\n",
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              "      <td>49.599850</td>\n",
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              "      <td>133.999588</td>\n",
              "      <td>62.299862</td>\n",
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              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>34.099998</td>\n",
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              "      <td>16.999998</td>\n",
              "      <td>68.500000</td>\n",
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              "      <td>63.799999</td>\n",
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              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>34.099998</td>\n",
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              "      <td>68.500000</td>\n",
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              "      <td>134.000000</td>\n",
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              "    <tr>\n",
              "      <th>356</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>357</th>\n",
              "      <td>17.049999</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>8.499999</td>\n",
              "      <td>34.250000</td>\n",
              "      <td>3.050000</td>\n",
              "      <td>10.250000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>31.900000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.20000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.450000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.700000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.200000</td>\n",
              "      <td>19.299999</td>\n",
              "      <td>29.499998</td>\n",
              "      <td>22.499998</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>8.850000</td>\n",
              "      <td>27.200001</td>\n",
              "      <td>8.600000</td>\n",
              "      <td>21.549999</td>\n",
              "      <td>35.450001</td>\n",
              "      <td>40.099998</td>\n",
              "      <td>10.300000</td>\n",
              "      <td>4.700000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>24.799999</td>\n",
              "      <td>1.650000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.349999</td>\n",
              "      <td>0.350000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.149999</td>\n",
              "      <td>2.400000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>24.999998</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>31.149998</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.55000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>11.400000</td>\n",
              "      <td>7.950000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>357 rows × 122 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           1          2          3          4    ...       119       120  121  122\n",
              "1     0.000000   0.000000   0.000000   0.000000  ...  0.000000  0.000000  0.0  0.0\n",
              "2     0.000000   0.000000   0.000000   0.000000  ...  0.000000  0.000000  0.0  0.0\n",
              "3     0.000000   0.000000   0.000000  21.500555  ...  0.000000  0.000000  0.0  0.0\n",
              "4     0.000000   0.000000   0.000000   0.000000  ...  0.000000  0.000000  0.0  0.0\n",
              "5     0.000000   0.000000   0.000000   0.000000  ...  0.000000  0.000000  0.0  0.0\n",
              "..         ...        ...        ...        ...  ...       ...       ...  ...  ...\n",
              "353  34.099937  12.399961  17.000019  68.500015  ...  0.199999  0.999997  0.0  0.0\n",
              "354  34.099998  12.400000  16.999998  68.500000  ...  0.200000  1.000000  0.0  0.0\n",
              "355  34.099998  12.400000  17.000000  68.500000  ...  0.200000  1.000000  0.0  0.0\n",
              "356   0.000000   0.000000   0.000000   0.000000  ...  0.000000  0.000000  0.0  0.0\n",
              "357  17.049999   6.200000   8.499999  34.250000  ...  0.100000  0.500000  0.0  0.0\n",
              "\n",
              "[357 rows x 122 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cvxUWqtRDERS"
      },
      "source": [
        "#Function for creating the data\n",
        "def train_data(data,s):\n",
        "  X_train = np.zeros((data.shape[0]+2,data.shape[1]-2))\n",
        "  for day in range(3,data.shape[1]+1):\n",
        "    for location in range(1,data.shape[0]+1):\n",
        "      X_train[location-1][day-3]=data[day][location]\n",
        "      X_train[data.shape[0]-1][day-3] = data[day-1][s]\n",
        "      X_train[data.shape[0]][day-3] = data[day-2][s]\n",
        "\n",
        "  X_train = np.delete(X_train,(s-1),axis=0)\n",
        "\n",
        "  y_train= np.zeros((1,data.shape[1]-2))\n",
        "  for day in range(1,data.shape[1]+1):\n",
        "    y_train[0][day-3] = data[day][s]\n",
        "\n",
        "  return X_train,y_train"
      ],
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xc_AYon1ze5d"
      },
      "source": [
        "#Creating the training data for Bombay using train_data function\n",
        "X_train_bombay,y_train_bombay = train_data(rainfall_2010,42)\n",
        "\n",
        "#Creating the test data for Bombay using train_data function\n",
        "X_test_bombay,y_test_bombay = train_data(rainfall_2011,42)"
      ],
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9KxpqaBcOJIE"
      },
      "source": [
        "#Creating the training data for Delhi using train_data function\n",
        "X_train_delhi,y_train_delhi=train_data(rainfall_2010,158)\n",
        "\n",
        "#Creating the training data for Delhi using train_data function\n",
        "X_test_delhi,y_test_delhi=train_data(rainfall_2011,158)"
      ],
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkx4k2UdOSHr"
      },
      "source": [
        "#Creating the training data for Kharagpur using train_data function\n",
        "X_train_kgp,y_train_kgp = train_data(rainfall_2010,299)\n",
        "\n",
        "#Creating the training data for Kharagpur using train_data function\n",
        "X_test_kgp,y_test_kgp = train_data(rainfall_2011,299)"
      ],
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FdRjXHXjzlTA"
      },
      "source": [
        "#Function to determine parameters and bias for training data using Linear Regression\n",
        "def Linear_Regression(X_train,y_train):\n",
        "  reg = LinearRegression() \n",
        "  reg.fit(X_train.T, y_train.T)\n",
        "  return reg.coef_,reg.intercept_"
      ],
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oK0C_m5uOaK5"
      },
      "source": [
        "#Determine the parameters and bias for Bombay Training Data Set\n",
        "w_bombay,b_bombay = Linear_Regression(X_train_bombay,y_train_bombay)\n",
        "\n",
        "#Determine the parameters and bias for Delhi Training Data Set\n",
        "w_delhi,b_delhi = Linear_Regression(X_train_delhi,y_train_delhi)\n",
        "\n",
        "#Determine the parameters and bias for Kharagpur Training Data Set\n",
        "w_kgp,b_kgp = Linear_Regression(X_train_kgp,y_train_kgp)"
      ],
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qzFOOIrFOfC1"
      },
      "source": [
        "#Function to determine Mean Squared Error and Mean Absolute error\n",
        "def mean_sq_abs_error(w,b,X_test,y_test):\n",
        "  y_pred = b + np.dot(w,X_test)\n",
        "  return (mean_squared_error(y_test,y_pred),mean_absolute_error(y_test,y_pred))"
      ],
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k1U91ebkOiHz"
      },
      "source": [
        "#Determine the Mean Squared Error and Mean Absolute Error for Bombay\n",
        "mse_bombay,mbe_bombay = mean_sq_abs_error(w_bombay,b_bombay,X_test_bombay,y_test_bombay)\n",
        "\n",
        "#Determine the Mean Squared Error and Mean Absolute Error for Delhi\n",
        "mse_delhi,mbe_delhi = mean_sq_abs_error(w_delhi,b_delhi,X_test_delhi,y_test_delhi)\n",
        "\n",
        "#Determine the Mean Squared Error and Mean Absolute Error for Kharagpur\n",
        "mse_kgp,mbe_kgp = mean_sq_abs_error(w_kgp,b_kgp,X_test_kgp,y_test_kgp)"
      ],
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TtqAir2gQS5h",
        "outputId": "fc38765e-1ac2-4a86-8f49-612674e4665e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "#Printing the Mean Squared Error for Kharagpur,Delhi and Bombay\n",
        "print(\"The Mean Absolute Error for Bombay: \",mbe_bombay)\n",
        "print(\"The Mean Sqaure Error for Bombay: \",mse_bombay)\n",
        "\n",
        "print()\n",
        "#Printing the Mean Squared Error and Mean Absolute Error for Delhi\n",
        "print(\"The Mean Absolute Error for Delhi: \",mbe_delhi)\n",
        "print(\"The Mean Sqaure Error for Delhi: \",mse_delhi)\n",
        "\n",
        "print()\n",
        "#Printing the Mean Squared Error and Mean Absolute Error for Delhi\n",
        "print(\"The Mean Absolute Error for Kharagpur: \",mbe_kgp)\n",
        "print(\"The Mean Sqaure Error for Kharagpur: \",mse_kgp)"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Mean Absolute Error for Bombay:  27.123974605327664\n",
            "The Mean Sqaure Error for Bombay:  1307.317836518758\n",
            "\n",
            "The Mean Absolute Error for Delhi:  15.616612309509373\n",
            "The Mean Sqaure Error for Delhi:  456.2140140776813\n",
            "\n",
            "The Mean Absolute Error for Kharagpur:  17.034490349554805\n",
            "The Mean Sqaure Error for Kharagpur:  519.9075235893562\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yfmj6OkBYEtG",
        "outputId": "219f5d16-6708-479b-affd-d07d06003934",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "source": [
        "#Lasso Regression for Bombay\n",
        "lasso = Lasso()\n",
        "coeff=[]\n",
        "alphas = [0.5, 1, 5, 10,39, 50, 100]\n",
        "\n",
        "#Finding the coeff for different alphas \n",
        "for a in alphas:\n",
        "    lasso.set_params(alpha=a)\n",
        "    lasso.fit(X_train_bombay.T, y_train_bombay.T)\n",
        "    coeff.append(lasso.coef_)\n",
        "\n",
        "#Plotting Co-efficients vs Lambda\n",
        "plt.figure(figsize=(7,5))   \n",
        "ax = plt.gca()\n",
        "ax.plot(alphas, coeff)\n",
        "ax.set_xscale('log')\n",
        "plt.axis('tight')\n",
        "plt.xlabel('Lambda')\n",
        "plt.ylabel('Co-efficients')"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Co-efficients')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJpjqRyYplJA",
        "outputId": "a7bd06c9-226e-41c6-8597-7f30ea4fd60d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Finding the most important features for Bombay Rainfall\n",
        "most_imp_bombay={}\n",
        "for i in range(len(coeff[4])):\n",
        "  if coeff[4][i]!=0:\n",
        "    most_imp_bombay[i+3]=coeff[4][i]\n",
        "most_imp_bombay.pop(min(most_imp_bombay))\n",
        "print(\"The most important features for Bombay are:\")\n",
        "print('Days:',end=' ')\n",
        "print(*list(most_imp_bombay.keys()),sep=',')"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The most important features for Bombay are:\n",
            "Days: 8,28,42,43,318\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CPyYC0hYEaB",
        "outputId": "d6c22a90-cf40-4e91-a31f-1879dad0c890",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "source": [
        "#Lasso Regression for Delhi\n",
        "lasso = Lasso()\n",
        "coeff_delhi=[]\n",
        "alphas = [0.5, 1, 5, 10, 50,70, 100]\n",
        "\n",
        "#Finding the coeff for different alphas \n",
        "for a in alphas:\n",
        "    lasso.set_params(alpha=a)\n",
        "    lasso.fit(X_train_delhi.T, y_train_delhi.T)\n",
        "    coeff_delhi.append(lasso.coef_)\n",
        "\n",
        "#Plotting Co-efficients vs Lambda\n",
        "plt.figure(figsize=(7,5))   \n",
        "ax = plt.gca()\n",
        "ax.plot(alphas, coeff_delhi)\n",
        "ax.set_xscale('log')\n",
        "plt.axis('tight')\n",
        "plt.xlabel('Lambda')\n",
        "plt.ylabel('Co-efficients')"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11.491167385728687, tolerance: 2.7930952380921577\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.016303616952882, tolerance: 2.7930952380921577\n",
            "  positive)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Co-efficients')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZUSEW-ZnfSK1",
        "outputId": "fa5245d9-0bd5-4b0e-eb97-e01cdd47eb8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Finding the most important features for Delhi Rainfall\n",
        "most_imp_delhi={}\n",
        "for i in range(len(coeff_delhi[4])):\n",
        "  if coeff_delhi[4][i]!=0:\n",
        "    most_imp_delhi[i+1]=coeff_delhi[4][i]\n",
        "print(\"The most important features for Delhi are:\")\n",
        "print('Days:',end=' ')\n",
        "print(list(most_imp_delhi.keys()),sep=',')"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The most important features for Delhi are:\n",
            "Days: [26, 59, 133, 159, 161, 215, 320]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5DkiDd2YEj4",
        "outputId": "0ac9b09f-bbc6-41f1-989c-af05dc7ae3c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "source": [
        "#Lasso Regression for Kharagpur\n",
        "lasso = Lasso()\n",
        "coeff_kgp =[]\n",
        "alphas = [0.5, 1, 5, 10, 30, 50 , 100]\n",
        "\n",
        "#Finding the coeff for different alphas \n",
        "for a in alphas:\n",
        "    lasso.set_params(alpha=a)\n",
        "    lasso.fit(X_train_kgp.T, y_train_kgp.T)\n",
        "    coeff_kgp.append(lasso.coef_)\n",
        "\n",
        "#Plotting Co-efficients vs Lambda\n",
        "plt.figure(figsize=(7,5))   \n",
        "ax = plt.gca()\n",
        "ax.plot(alphas, coeff_kgp)\n",
        "ax.set_xscale('log')\n",
        "plt.axis('tight')\n",
        "plt.xlabel('Lambda')\n",
        "plt.ylabel('Co-efficients')"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.636358380228842, tolerance: 2.220230646391177\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.384110484671737, tolerance: 2.220230646391177\n",
            "  positive)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Co-efficients')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w6RB_73GmSPj",
        "outputId": "9ffcbac1-fb32-4380-e43e-6d955155f728",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "most_imp_kgp={}\n",
        "for i in range(len(coeff_kgp[4])):\n",
        "  if coeff_kgp[4][i]!=0:\n",
        "    most_imp_kgp[i+3]=coeff_kgp[4][i]\n",
        "print(\"The most important features for Kharagpur are:\")\n",
        "print('Days:',end = ' ')\n",
        "print(*list(most_imp_kgp.keys()),sep=',')"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The most important features for Kharagpur are:\n",
            "Days: 87,164,233,235,250,280,292,300,306\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MyoE0KjsuJWa"
      },
      "source": [
        "XR1= rainfall_2010_data['XR1']\n",
        "ZR1= rainfall_2010_data['ZR1']\n",
        "#Using The Decision Tree Regressor Model for given training data(2010 Rainfall  Data)\n",
        "clf=DecisionTreeClassifier(random_state=0)\n",
        "\n",
        "#Fitting the training data\n",
        "clf.fit(XR1.T,ZR1.T)\n",
        "\n",
        "#Determining the feature importance using the corresponding values\n",
        "features=(clf.feature_importances_).reshape(357,1)"
      ],
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ICJnPEEcvB3i",
        "outputId": "808a94bb-a94f-4254-b213-662ad10d0a01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "discriminative_features=[]\n",
        "for i in range(features.shape[0]):\n",
        "  if(features[i][0]>0.0):\n",
        "    discriminative_features.append(i+1)\n",
        "print(\"Following are the index of the most discriminative features:\",discriminative_features)"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Following are the index of the most discriminative features: [27, 28, 67, 160, 163, 185, 206, 253, 347]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOPJgR2FXkrx",
        "outputId": "1bb1fac8-a15b-45a4-927c-015c15838dc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ZR2=(rainfall_2011_data['ZR']).reshape(122,1)\n",
        "XR2=rainfall_2011_data['XR']\n",
        "y_predict=(clf.predict(XR2.T)).reshape(122,1)\n",
        "accuracy=0\n",
        "for i in range(y_predict.shape[0]):\n",
        "  if(y_predict[i]==ZR2[i]):\n",
        "    accuracy+=1\n",
        "accuracy=accuracy/y_predict.shape[0]*100\n",
        "\n",
        "print(\"Accuracy of given Decision Tree Model on Test Data:\",accuracy)"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of given Decision Tree Model on Test Data: 71.31147540983606\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bBGex9mvX1RN"
      },
      "source": [
        ""
      ],
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJDBXnQdjpLB"
      },
      "source": [
        ""
      ],
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVJn9wl9jo8P"
      },
      "source": [
        ""
      ],
      "execution_count": 160,
      "outputs": []
    }
  ]
}